Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.

翻译：模式识别与图像分类是机器视觉中的核心任务。例如，自动驾驶车辆需要能够采集动态环境中包含的复杂信息并进行实时分类。本文中，我们通过实验展示了将单像素成像（SPI）技术与低复杂度机器学习模型相结合，实现数千赫兹帧率的图像分类。采用微LED-on-CMOS数字光投影器进行SPI，可实现亚毫秒级图像编码的超快图案生成。我们针对广泛接受的MNIST手写数字分类基准任务，评估了实验系统的分类准确率。比较了两种机器学习模型的分类性能：极限学习机（ELM）与基于反向传播训练的深度神经网络。两种模型均保持较低复杂度，使得推理时间增加的开销与图像生成时间相当。关键的是，我们的单像素图像分类方法基于信息的时空变换，完全无需图像重建过程。通过探索基于SPI的ELM作为二分类器的性能，我们证明了其在超快成像场景中实现高效异常检测的潜力。